{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "import torch \n",
    "import numpy\n",
    "from matplotlib import pyplot as plt "
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "def createDataset():\n",
    "    data=numpy.array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]]) # torch tensor 只可以存储数值\n",
    "    labels=['A','A','B','B']\n",
    "    return data,labels\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "source": [
    "dataset,labels=createDataset()\n",
    "x=[]\n",
    "y=[]\n",
    "# print(dataset.numpy())\n",
    "for a,b in dataset:\n",
    "    x.append(a)\n",
    "    y.append(b)\n",
    "print(x)\n",
    "print(y)\n",
    "plt.plot(x,y,'ro')\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[1.0, 1.0, 0.0, 0.0]\n",
      "[1.1, 1.0, 0.0, 0.1]\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
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   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "def classifyK(input,dataset,labels,k):\n",
    "    dataset_size=dataset.shape[0]\n",
    "    "
   ],
   "outputs": [],
   "metadata": {}
  }
 ],
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